serving/tensorflow_serving/g3doc/setup.md at master

Installation

Prerequisites

To compile and use TensorFlow Serving, you need to set up some prerequisites.

Bazel

TensorFlow Serving requires Bazel 0.3.2 or higher. You can find the Bazel installation instructions here.

If you have the prerequisites for Bazel, those instructions consist of the following steps:

Download the relevant binary from here. Let's say you downloaded bazel-0.3.2-installer-linux-x86_64.sh. You would execute:
```
cd ~/Downloads
chmod +x bazel-0.3.2-installer-linux-x86_64.sh
./bazel-0.3.2-installer-linux-x86_64.sh --user
```
Set up your environment. Put this in your ~/.bashrc.
```
export PATH="$PATH:$HOME/bin"
```

gRPC

Our tutorials use gRPC (1.0.0 or higher) as our RPC framework. You can find the installation instructions here.

Packages

To install TensorFlow Serving dependencies, execute the following:

sudo apt-get update && sudo apt-get install -y \
        build-essential \
        curl \
        libcurl3-dev \
        git \
        libfreetype6-dev \
        libpng12-dev \
        libzmq3-dev \
        pkg-config \
        python-dev \
        python-numpy \
        python-pip \
        software-properties-common \
        swig \
        zip \
        zlib1g-dev

Installing from source

Clone the TensorFlow Serving repository

git clone --recurse-submodules https://github.com/tensorflow/serving
cd serving

--recurse-submodules is required to fetch TensorFlow, gRPC, and other libraries that TensorFlow Serving depends on. Note that these instructions will install the latest master branch of TensorFlow Serving. If you want to install a specific branch (such as a release branch), pass -b <branchname> to the git clone command.

Install prerequisites

Follow the Prerequisites section above to install all dependencies. To configure TensorFlow, run

cd tensorflow
./configure
cd ..

Consult the TensorFlow install instructions if you encounter any issues with setting up TensorFlow or its dependencies.

Build

TensorFlow Serving uses Bazel to build. Use Bazel commands to build individual targets or the entire source tree.

To build the entire tree, execute:

bazel build tensorflow_serving/...

Binaries are placed in the bazel-bin directory, and can be run using a command like:

./bazel-bin/tensorflow_serving/example/mnist_inference

To test your installation, execute:

bazel test tensorflow_serving/...

See the basic tutorial and advanced tutorial for more in-depth examples of running TensorFlow Serving.

Continuous integration build

Our continuous integration build using TensorFlow ci_build infrastructure offers you simplified development using docker. All you need is git and docker. No need to install all other dependencies manually.

git clone --recursive https://github.com/tensorflow/serving
cd serving
CI_TENSORFLOW_SUBMODULE_PATH=tensorflow tensorflow/tensorflow/tools/ci_build/ci_build.sh CPU bazel test //tensorflow_serving/...

Note: The serving directory is mapped into the container. You can develop outside the docker container (in your favourite editor) and when you run this build it will build with your changes.